Many computer processing and data storage systems receive a large volume of messages that are processed and stored in a database of information. Systems such as logistics management systems, manufacturing systems, retail systems, medical systems, and other data systems may receive thousands or millions of messages a day that need to be processed and stored in the processing and data storage system. For example, medical processing and data storage systems (e.g., Amalga Unified Intelligence System by Microsoft Corporation) may receive a large number of messages regarding patient transactions, patient procedures, medical test, various types of billing and a large number of other types of medical information.
Due the volume of data that is received and stored in many large data processing and data storage systems, there can be difficulties in querying the data because the system does not have a knowledge of the meaning of incoming data. A human may manually tag each data element with a semantic tag, and then manually create database query views that expose this data in the schema expected by the user application. However, this type of manual tagging and query generation is labor intensive and expensive in order to apply a semantic tag to the data elements.